Big Data: It’s Not Just for Breakfast Anymore

Is it time to upgrade financial services? Image: ota_photos/Flickr

Banking as an industry hasn’t seen major disruption in decades. Certainly there have been improvements and enhancements along the way, but the fundamental process of banking is the same as it was 40 years ago. Given the incredible leaps in computer technology that we’ve made over the past decades, this makes no sense. Financial services are a key part of modern life: We can, and should, do more to innovate in this industry.

Take consumer lending: There’s been virtually no major innovation in credit underwriting, the process of judging loan eligibility, since the 1970s.

In the early-1970s, Fair Isaac rose to global prominence as a provider of the FICO score, which supplanted much of the credit officers’ role. The standardized score massively increased credit availability and thereby lowered the cost of borrowing. This was the last radical transformation of consumer finance.

However, FICO scores have their limits. They perform especially poorly for those without much information in their credit files, or those with relatively bad credit. It’s not FICO’s fault — it happens because of the math they use; they can only use a small number of different variables to make each credit decision. Yet as a society, we are generating more data than at any other time in history. Failing to use these volumes of data blocks millions of people — the “underbanked” — from having access to fair, transparent, and lower cost credit.

The Power of Big Data

Applying new technology to a traditional system can have meaningful impact on the banking industry, and on millions of people’s lives. Companies like Google, Amazon and Netflix have transformed their industries by using big data to inform business decisions. Innovative technologies — particularly the oft-hyped big data — have the potential to do the same for financial services.

The common perception of “big data” is that you can track high volumes of transactions. But in the context of consumer lending, looking at more transactions in the same way wouldn’t help.

However, adding more signals — analyzing new types of data — will help. Don’t count more of the same things; count different things.

Rather than depending on the traditional handful of pieces of data that lenders typically use, we should use thousands of signals. This is what my startup, ZestFinance, has been focused on; we have demonstrated that using more signals yields far better underwriting outcomes than traditional means, especially, though not exclusively, for the underbanked.

We first introduced big data analysis to underwriting, and now we’re bringing these same techniques to the collections process.

Why collections? Aren’t collectors evil, treating borrowers badly? Why should one help collectors be more efficient?

Great questions, with a surprising answer: Good collections processing is good for borrowers.

Huh? That can’t be correct.

But it is. When borrowers have difficulty making a payment, they often interact with a collector. Most borrowers actually want to pay back their loans. Often, these collectors are able to work with borrowers to, for example, work out a payment plan so that the borrowers can get back on track. But some borrowers are unlikely to be able to get back on track — sometimes truly awful financial shocks happen to borrowers and they aren’t going to recover for some time. If a borrower is never really going to be able to repay, should that borrower get lots of collector calls, day after day, causing increased stress and even fear of future outcomes? And should a collector spend its resources trying to collect that loan? No.

We’ve found that a big data approach to collections works as well as the big data approach to underwriting: A model with more signals enables a collector to understand which customers are going to pay back and can help structure a payment plan. We have demonstrated such models in a variety of collections verticals — the approach works. The new signals may surprise you, but they work. In student loan collections, people with more addresses since they graduated from college pay back better. Unless, that is, they change those addresses surprisingly quickly, in which case they pay back far worse. If the borrower has moved far from their college, they pay back slightly better than those who remain close. No, I don’t know why, but geography seems to matter.

Better Data, Better Service

By incorporating advanced technology, financial institutions can incorporate more signals and make more accurate credit decisions that will help them serve their customers better and extend more credit to people who need it. Likewise, collections companies can re-focus their efforts on borrowers who actually can repay their debt, resulting in a better debtor experience.

When I was at Google, we figured out how to use vast amounts of data, and really new math techniques to make information more accessible and useful. We can apply similar machine learning techniques and large-scale data analysis to other sectors to create meaningful disruption, and I can think of no better place to see change than the financial services industry.

Douglas Merrill is CEO and founder of ZestFinance, a technology startup that is transforming credit underwriting with big data analysis. He was previously CIO and VP of engineering at Google.

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